Comparative performance of multi-period ACOPF and multi-period DCOPF under high integration of wind power
Today, the power system operation represents a challenge given the security and reliability requirements. Mathematical models are used to represent and solve operational and planning issues related with electric systems. Specifically, the AC optimal power flow (ACOPF) and the DC optimal power flow (...
- Autores:
-
Larrahondo, Diego
Moreno-Chuquen, Ricardo
Chamorro, Harold R.
Gonzalez-Longatt, Francisco
- Tipo de recurso:
- Article of journal
- Fecha de publicación:
- 2021
- Institución:
- Universidad Autónoma de Occidente
- Repositorio:
- RED: Repositorio Educativo Digital UAO
- Idioma:
- eng
- OAI Identifier:
- oai:red.uao.edu.co:10614/13739
- Acceso en línea:
- https://hdl.handle.net/10614/13739
- Palabra clave:
- Energía eólica
Recursos energéticos renovables
Modelos matemáticos
Wind power
Renewable energy sources
Mathematical models
Optimal power flow
Renewable energy
DCOPF
ACOPF
- Rights
- openAccess
- License
- Derechos Reservados MDPI
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| dc.title.eng.fl_str_mv |
Comparative performance of multi-period ACOPF and multi-period DCOPF under high integration of wind power |
| title |
Comparative performance of multi-period ACOPF and multi-period DCOPF under high integration of wind power |
| spellingShingle |
Comparative performance of multi-period ACOPF and multi-period DCOPF under high integration of wind power Energía eólica Recursos energéticos renovables Modelos matemáticos Wind power Renewable energy sources Mathematical models Optimal power flow Renewable energy DCOPF ACOPF |
| title_short |
Comparative performance of multi-period ACOPF and multi-period DCOPF under high integration of wind power |
| title_full |
Comparative performance of multi-period ACOPF and multi-period DCOPF under high integration of wind power |
| title_fullStr |
Comparative performance of multi-period ACOPF and multi-period DCOPF under high integration of wind power |
| title_full_unstemmed |
Comparative performance of multi-period ACOPF and multi-period DCOPF under high integration of wind power |
| title_sort |
Comparative performance of multi-period ACOPF and multi-period DCOPF under high integration of wind power |
| dc.creator.fl_str_mv |
Larrahondo, Diego Moreno-Chuquen, Ricardo Chamorro, Harold R. Gonzalez-Longatt, Francisco |
| dc.contributor.author.none.fl_str_mv |
Larrahondo, Diego Moreno-Chuquen, Ricardo Chamorro, Harold R. Gonzalez-Longatt, Francisco |
| dc.subject.armarc.spa.fl_str_mv |
Energía eólica Recursos energéticos renovables Modelos matemáticos |
| topic |
Energía eólica Recursos energéticos renovables Modelos matemáticos Wind power Renewable energy sources Mathematical models Optimal power flow Renewable energy DCOPF ACOPF |
| dc.subject.armarc.eng.fl_str_mv |
Wind power Renewable energy sources Mathematical models |
| dc.subject.proposal.eng.fl_str_mv |
Optimal power flow Renewable energy DCOPF ACOPF |
| description |
Today, the power system operation represents a challenge given the security and reliability requirements. Mathematical models are used to represent and solve operational and planning issues related with electric systems. Specifically, the AC optimal power flow (ACOPF) and the DC optimal power flow (DCOPF) are tools used for operational and planning purposes. The DCOPF versions correspond to lineal versions of the ACOPF. This is due to the fact that the power flow solution is often hard to obtain with the ACOPF considering all constraints. However, the simplifications use only active power without considering reactive power, voltage values and losses on transmission lines, which are crucial factors for power system operation, potentially leading to inaccurate results. This paper develops a detailed formulation for both DCOPF and ACOPF with multiple generation sources to provide a 24-h dispatching in order to compare the differences between the solutions with different scenarios under high penetration of wind power. The results indicate the DCOPF inaccuracies with respect to the complete solution provided by the ACOPF |
| publishDate |
2021 |
| dc.date.issued.none.fl_str_mv |
2021-07 |
| dc.date.accessioned.none.fl_str_mv |
2022-04-06T18:57:28Z |
| dc.date.available.none.fl_str_mv |
2022-04-06T18:57:28Z |
| dc.type.spa.fl_str_mv |
Artículo de revista |
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http://purl.org/coar/resource_type/c_2df8fbb1 |
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http://purl.org/coar/version/c_970fb48d4fbd8a85 |
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http://purl.org/coar/resource_type/c_6501 |
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Energies |
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https://hdl.handle.net/10614/13739 |
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19961073 |
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Energies 19961073 |
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https://hdl.handle.net/10614/13739 |
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eng |
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eng |
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15 |
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15 |
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1 |
| dc.relation.citationvolume.spa.fl_str_mv |
14 |
| dc.relation.cites.none.fl_str_mv |
Larrahondo, D., Moreno, R., Chamorro, H. R., González Longatt, F. (2021). Comparative Performance of Multi-Period ACOPF and Multi-Period DCOPF under High Integration of Wind Power. Energies. Vol. 14 (15), pp. 1-15. |
| dc.relation.references.none.fl_str_mv |
Foley, A.; Olabi, A.G. Renewable energy technology developments, trends and policy implications that can underpin the drive for global climate change. Renew. Sustain. Energy Rev. 2017, 68, 1112–1114. Hamels, S. CO2 Intensities and Primary Energy Factors in the Future European Electricity System. Energies 2021, 14, 2165 Nasirov, S.; Cruz, E.; Agostini, C.A.; Silva, C. Policy Makers’ Perspectives on the Expansion of Renewable Energy Sources in Chile’s Electricity Auctions. Energies 2019, 12, 4149 Carrasco, J.M.; Franquelo, L.G.; Bialasiewicz, J.T.; Galván, E.; PortilloGuisado, R.C.; Prats, M.M.; León, J.I.; Moreno-Alfonso, N. Power-electronic systems for the grid integration of renewable energy sources: A survey. IEEE Trans. Ind. Electron. 2006, 53, 1002–1016 Moreno, R.; Hoyos, C.; Cantillo, S. A Framework from Peer-to-Peer Electricity Trading Based on Communities Transactions. Int. J. Energy Econ. Policy (IJEEP) 2021, 11, 537–545 Shariatmadar, K.; Arrigo, A.; Vallée, F.; Hallez, H.; Vandevelde, L.; Moens, D. Day-Ahead Energy and Reserve Dispatch Problem under Non-Probabilistic Uncertainty. Energies 2021, 14, 1016. Capitanescu, F.; Ramos, J.M.; Panciatici, P.; Kirschen, D.; Marcolini, A.M.; Platbrood, L.; Wehenkel, L. State-of-the-art, challenges, and future trends in security constrained optimal power flow. Electr. Power Syst. Res. 2011, 81, 1731–1741 Moreno, R. Identification of Topological Vulnerabilities for Power Systems Networks. In Proceedings of the 2018 IEEE Power & Energy Society General Meeting (PESGM), Portland, OR, USA, 5–9 August 2018; doi:10.1109/PESGM.2018.8586143 Momoh, J.A. Electric Power System Applications of Optimization; CRC Press: Boca Raton, FL, USA, 2017 Zimmerman, R.D.; Murillo-Sánchez, C.E.; Thomas, R.J. MATPOWER: Steady-state operations, planning, and analysis tools for power systems research and education. IEEE Trans. Power Syst. 2010, 26, 12–19 Kang, S.; Kim, J.; Park, J.W.; Baek, S.M. Reactive power management based on voltage sensitivity analysis of distribution system with high penetration of renewable energies. Energies 2019, 12, 1493 Dall’Anese, E.; Baker, K.; Summers, T. Chance-constrained AC optimal power flow for distribution systems with renewables. IEEE Trans. Power Syst. 2017, 32, 3427–3438 Ochoa, L.F.; Harrison, G.P. Minimizing energy losses: Optimal accommodation and smart operation of renewable distributed generation. IEEE Trans. Power Syst. 2010, 26, 198–205 Bai, W.; Lee, D.; Lee, K.Y. Stochastic dynamic AC optimal power flow based on a multivariate short-term wind power scenario forecasting model. Energies 2017, 10, 2138 Montoya, O.D.; Grisales-Noreña, L.; González-Montoya, D.; Ramos-Paja, C.; Garces, A. Linear power flow formulation for low-voltage DC power grids. Electr. Power Syst. Res. 2018, 163, 375–381 Jabr, R.A. Adjustable Robust OPF With Renewable Energy Sources. IEEE Trans. Power Syst. 2013, 28, 4742–4751. Obando, J.S.; González, G.; Moreno, R. Quantification of operating reserves with high penetration of wind power considering extreme values. Int. J. Electr. Comput. Eng. (IJECE) 2020 Soroush, M.; Fuller, J.D. Accuracies of optimal transmission switching heuristics based on DCOPF and ACOPF. IEEE Trans. Power Syst. 2013, 29, 924–932 Dunning, I.; Huchette, J.; Lubin, M. JuMP: A modeling language for mathematical optimization. SIAM Rev. 2017, 59, 295–320. |
| dc.rights.spa.fl_str_mv |
Derechos Reservados MDPI |
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https://creativecommons.org/licenses/by-nc-nd/4.0/ |
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info:eu-repo/semantics/openAccess |
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Atribución-NoComercial-SinDerivadas 4.0 Internacional (CC BY-NC-ND 4.0) |
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Derechos Reservados MDPI https://creativecommons.org/licenses/by-nc-nd/4.0/ Atribución-NoComercial-SinDerivadas 4.0 Internacional (CC BY-NC-ND 4.0) http://purl.org/coar/access_right/c_abf2 |
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Larrahondo, Diegobcb50cab262bc119ecc64ad3b6618ab9Moreno-Chuquen, Ricardof36efacf1d947d7410ab7d332d414753Chamorro, Harold R.9a051938450f9dd5019a0b674dd82e48Gonzalez-Longatt, Franciscod7dc2c674113728ddd354d9345b13f862022-04-06T18:57:28Z2022-04-06T18:57:28Z2021-07Energieshttps://hdl.handle.net/10614/1373919961073Today, the power system operation represents a challenge given the security and reliability requirements. Mathematical models are used to represent and solve operational and planning issues related with electric systems. Specifically, the AC optimal power flow (ACOPF) and the DC optimal power flow (DCOPF) are tools used for operational and planning purposes. The DCOPF versions correspond to lineal versions of the ACOPF. This is due to the fact that the power flow solution is often hard to obtain with the ACOPF considering all constraints. However, the simplifications use only active power without considering reactive power, voltage values and losses on transmission lines, which are crucial factors for power system operation, potentially leading to inaccurate results. This paper develops a detailed formulation for both DCOPF and ACOPF with multiple generation sources to provide a 24-h dispatching in order to compare the differences between the solutions with different scenarios under high penetration of wind power. The results indicate the DCOPF inaccuracies with respect to the complete solution provided by the ACOPF16 páginasapplication/pdfengMDPIDerechos Reservados MDPIhttps://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccessAtribución-NoComercial-SinDerivadas 4.0 Internacional (CC BY-NC-ND 4.0)http://purl.org/coar/access_right/c_abf2Comparative performance of multi-period ACOPF and multi-period DCOPF under high integration of wind powerArtículo de revistahttp://purl.org/coar/resource_type/c_6501http://purl.org/coar/resource_type/c_2df8fbb1Textinfo:eu-repo/semantics/articlehttp://purl.org/redcol/resource_type/ARTinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/version/c_970fb48d4fbd8a85Energía eólicaRecursos energéticos renovablesModelos matemáticosWind powerRenewable energy sourcesMathematical modelsOptimal power flowRenewable energyDCOPFACOPF1515114Larrahondo, D., Moreno, R., Chamorro, H. R., González Longatt, F. (2021). Comparative Performance of Multi-Period ACOPF and Multi-Period DCOPF under High Integration of Wind Power. Energies. Vol. 14 (15), pp. 1-15.Foley, A.; Olabi, A.G. Renewable energy technology developments, trends and policy implications that can underpin the drive for global climate change. Renew. Sustain. Energy Rev. 2017, 68, 1112–1114.Hamels, S. CO2 Intensities and Primary Energy Factors in the Future European Electricity System. Energies 2021, 14, 2165Nasirov, S.; Cruz, E.; Agostini, C.A.; Silva, C. Policy Makers’ Perspectives on the Expansion of Renewable Energy Sources in Chile’s Electricity Auctions. Energies 2019, 12, 4149Carrasco, J.M.; Franquelo, L.G.; Bialasiewicz, J.T.; Galván, E.; PortilloGuisado, R.C.; Prats, M.M.; León, J.I.; Moreno-Alfonso, N. Power-electronic systems for the grid integration of renewable energy sources: A survey. IEEE Trans. Ind. Electron. 2006, 53, 1002–1016Moreno, R.; Hoyos, C.; Cantillo, S. A Framework from Peer-to-Peer Electricity Trading Based on Communities Transactions. Int. J. Energy Econ. Policy (IJEEP) 2021, 11, 537–545Shariatmadar, K.; Arrigo, A.; Vallée, F.; Hallez, H.; Vandevelde, L.; Moens, D. Day-Ahead Energy and Reserve Dispatch Problem under Non-Probabilistic Uncertainty. Energies 2021, 14, 1016.Capitanescu, F.; Ramos, J.M.; Panciatici, P.; Kirschen, D.; Marcolini, A.M.; Platbrood, L.; Wehenkel, L. State-of-the-art, challenges, and future trends in security constrained optimal power flow. Electr. Power Syst. Res. 2011, 81, 1731–1741Moreno, R. Identification of Topological Vulnerabilities for Power Systems Networks. In Proceedings of the 2018 IEEE Power & Energy Society General Meeting (PESGM), Portland, OR, USA, 5–9 August 2018; doi:10.1109/PESGM.2018.8586143Momoh, J.A. Electric Power System Applications of Optimization; CRC Press: Boca Raton, FL, USA, 2017Zimmerman, R.D.; Murillo-Sánchez, C.E.; Thomas, R.J. MATPOWER: Steady-state operations, planning, and analysis tools for power systems research and education. IEEE Trans. Power Syst. 2010, 26, 12–19Kang, S.; Kim, J.; Park, J.W.; Baek, S.M. Reactive power management based on voltage sensitivity analysis of distribution system with high penetration of renewable energies. Energies 2019, 12, 1493Dall’Anese, E.; Baker, K.; Summers, T. Chance-constrained AC optimal power flow for distribution systems with renewables. IEEE Trans. Power Syst. 2017, 32, 3427–3438Ochoa, L.F.; Harrison, G.P. Minimizing energy losses: Optimal accommodation and smart operation of renewable distributed generation. IEEE Trans. Power Syst. 2010, 26, 198–205Bai, W.; Lee, D.; Lee, K.Y. Stochastic dynamic AC optimal power flow based on a multivariate short-term wind power scenario forecasting model. Energies 2017, 10, 2138Montoya, O.D.; Grisales-Noreña, L.; González-Montoya, D.; Ramos-Paja, C.; Garces, A. Linear power flow formulation for low-voltage DC power grids. Electr. Power Syst. Res. 2018, 163, 375–381Jabr, R.A. Adjustable Robust OPF With Renewable Energy Sources. IEEE Trans. Power Syst. 2013, 28, 4742–4751.Obando, J.S.; González, G.; Moreno, R. Quantification of operating reserves with high penetration of wind power considering extreme values. Int. J. Electr. Comput. Eng. (IJECE) 2020Soroush, M.; Fuller, J.D. Accuracies of optimal transmission switching heuristics based on DCOPF and ACOPF. IEEE Trans. Power Syst. 2013, 29, 924–932Dunning, I.; Huchette, J.; Lubin, M. JuMP: A modeling language for mathematical optimization. SIAM Rev. 2017, 59, 295–320.Comunidad universitaria en generalPublicationLICENSElicense.txtlicense.txttext/plain; charset=utf-81665https://dspace7-uao.metacatalogo.com/bitstreams/3eb96ecd-f9e5-4938-8a05-d69f014905b3/download20b5ba22b1117f71589c7318baa2c560MD52ORIGINALComparative Performance of Multi-Period ACOPF and Multi-Period DCOPF under High Integration of Wind Powe.pdfComparative Performance of Multi-Period ACOPF and Multi-Period DCOPF under High Integration of Wind Powe.pdfTexto archivo completo del artículo de revista, PDFapplication/pdf10660461https://dspace7-uao.metacatalogo.com/bitstreams/3f90b5d1-0f0d-43fb-a2ca-2de097db4089/download31571756de1642d8d9c0d58fc45cc83aMD53TEXTComparative Performance of Multi-Period ACOPF and Multi-Period DCOPF under High Integration of Wind Powe.pdf.txtComparative Performance of Multi-Period ACOPF and Multi-Period DCOPF under High Integration of Wind Powe.pdf.txtExtracted texttext/plain37377https://dspace7-uao.metacatalogo.com/bitstreams/1bf684f1-f7c7-4a46-bef3-5b0637ab1f50/downloada15f2a81bb7ab8611cf969999636f5b8MD54THUMBNAILComparative Performance of Multi-Period ACOPF and Multi-Period DCOPF under High Integration of Wind Powe.pdf.jpgComparative Performance of Multi-Period ACOPF and Multi-Period DCOPF under High Integration of Wind Powe.pdf.jpgGenerated Thumbnailimage/jpeg16319https://dspace7-uao.metacatalogo.com/bitstreams/115b469d-0026-40f4-973d-d8485496b1c5/downloada8afa538817f0de8811c028248209228MD5510614/13739oai:dspace7-uao.metacatalogo.com:10614/137392024-01-19 17:23:32.993https://creativecommons.org/licenses/by-nc-nd/4.0/Derechos Reservados MDPIopen.accesshttps://dspace7-uao.metacatalogo.comRepositorio UAOrepositorio@uao.edu.coRUwgQVVUT1IgYXV0b3JpemEgYSBsYSBVbml2ZXJzaWRhZCBBdXTDs25vbWEgZGUgT2NjaWRlbnRlLCBkZSBmb3JtYSBpbmRlZmluaWRhLCBwYXJhIHF1ZSBlbiBsb3MgdMOpcm1pbm9zIGVzdGFibGVjaWRvcyBlbiBsYSBMZXkgMjMgZGUgMTk4MiwgbGEgTGV5IDQ0IGRlIDE5OTMsIGxhIERlY2lzacOzbiBhbmRpbmEgMzUxIGRlIDE5OTMsIGVsIERlY3JldG8gNDYwIGRlIDE5OTUgeSBkZW3DoXMgbGV5ZXMgeSBqdXJpc3BydWRlbmNpYSB2aWdlbnRlIGFsIHJlc3BlY3RvLCBoYWdhIHB1YmxpY2FjacOzbiBkZSBlc3RlIGNvbiBmaW5lcyBlZHVjYXRpdm9zLiBQQVJBR1JBRk86IEVzdGEgYXV0b3JpemFjacOzbiBhZGVtw6FzIGRlIHNlciB2w6FsaWRhIHBhcmEgbGFzIGZhY3VsdGFkZXMgeSBkZXJlY2hvcyBkZSB1c28gc29icmUgbGEgb2JyYSBlbiBmb3JtYXRvIG8gc29wb3J0ZSBtYXRlcmlhbCwgdGFtYmnDqW4gcGFyYSBmb3JtYXRvIGRpZ2l0YWwsIGVsZWN0csOzbmljbywgdmlydHVhbCwgcGFyYSB1c29zIGVuIHJlZCwgSW50ZXJuZXQsIGV4dHJhbmV0LCBpbnRyYW5ldCwgYmlibGlvdGVjYSBkaWdpdGFsIHkgZGVtw6FzIHBhcmEgY3VhbHF1aWVyIGZvcm1hdG8gY29ub2NpZG8gbyBwb3IgY29ub2Nlci4gRUwgQVVUT1IsIGV4cHJlc2EgcXVlIGVsIGRvY3VtZW50byAodHJhYmFqbyBkZSBncmFkbywgcGFzYW50w61hLCBjYXNvcyBvIHRlc2lzKSBvYmpldG8gZGUgbGEgcHJlc2VudGUgYXV0b3JpemFjacOzbiBlcyBvcmlnaW5hbCB5IGxhIGVsYWJvcsOzIHNpbiBxdWVicmFudGFyIG5pIHN1cGxhbnRhciBsb3MgZGVyZWNob3MgZGUgYXV0b3IgZGUgdGVyY2Vyb3MsIHkgZGUgdGFsIGZvcm1hLCBlbCBkb2N1bWVudG8gKHRyYWJham8gZGUgZ3JhZG8sIHBhc2FudMOtYSwgY2Fzb3MgbyB0ZXNpcykgZXMgZGUgc3UgZXhjbHVzaXZhIGF1dG9yw61hIHkgdGllbmUgbGEgdGl0dWxhcmlkYWQgc29icmUgw6lzdGUuIFBBUkFHUkFGTzogZW4gY2FzbyBkZSBwcmVzZW50YXJzZSBhbGd1bmEgcmVjbGFtYWNpw7NuIG8gYWNjacOzbiBwb3IgcGFydGUgZGUgdW4gdGVyY2VybywgcmVmZXJlbnRlIGEgbG9zIGRlcmVjaG9zIGRlIGF1dG9yIHNvYnJlIGVsIGRvY3VtZW50byAoVHJhYmFqbyBkZSBncmFkbywgUGFzYW50w61hLCBjYXNvcyBvIHRlc2lzKSBlbiBjdWVzdGnDs24sIEVMIEFVVE9SLCBhc3VtaXLDoSBsYSByZXNwb25zYWJpbGlkYWQgdG90YWwsIHkgc2FsZHLDoSBlbiBkZWZlbnNhIGRlIGxvcyBkZXJlY2hvcyBhcXXDrSBhdXRvcml6YWRvczsgcGFyYSB0b2RvcyBsb3MgZWZlY3RvcywgbGEgVW5pdmVyc2lkYWQgIEF1dMOzbm9tYSBkZSBPY2NpZGVudGUgYWN0w7phIGNvbW8gdW4gdGVyY2VybyBkZSBidWVuYSBmZS4gVG9kYSBwZXJzb25hIHF1ZSBjb25zdWx0ZSB5YSBzZWEgZW4gbGEgYmlibGlvdGVjYSBvIGVuIG1lZGlvIGVsZWN0csOzbmljbyBwb2Ryw6EgY29waWFyIGFwYXJ0ZXMgZGVsIHRleHRvIGNpdGFuZG8gc2llbXByZSBsYSBmdWVudGUsIGVzIGRlY2lyIGVsIHTDrXR1bG8gZGVsIHRyYWJham8geSBlbCBhdXRvci4gRXN0YSBhdXRvcml6YWNpw7NuIG5vIGltcGxpY2EgcmVudW5jaWEgYSBsYSBmYWN1bHRhZCBxdWUgdGllbmUgRUwgQVVUT1IgZGUgcHVibGljYXIgdG90YWwgbyBwYXJjaWFsbWVudGUgbGEgb2JyYS4K |